Circular Cross Covariance
Updated 17 Aug 2004
CXCOV Circular Cross Covariance function estimates.
CXCOV(a,b), where a and b represent samples taken over time interval T, which is assumed to be a common period of two corresponding periodic signals.
a and b are supposed to be length M row vectors, either real or complex.
[x,c]=CXCOV(a,b) returns the length M-1 circular cross covariance sequence c with corresponding lags x.
The circular cross covariance is the normalized circular cross correlation function of two vectors with their means removed:
c(k) = sum[a(n)-mean(a))*conj(b(n+k)-mean(b))]/[norm(a-mean(a))*norm(b-mean(b))];
where vector b is shifted CIRCULARLY by k samples.
The function doesn't check the format of input vectors a and b!
For circular correlation between a and b look for CXCORR(a,b) in
A. V. Oppenheim, R. W. Schafer and J. R. Buck, Discrete-Time Signal Processing, Upper Saddler River, NJ : Prentice Hall, 1999.
Author: G. Levin, April 2004.
G. Levin (2023). Circular Cross Covariance (https://www.mathworks.com/matlabcentral/fileexchange/4811-circular-cross-covariance), MATLAB Central File Exchange. Retrieved .
MATLAB Release Compatibility
Platform CompatibilityWindows macOS Linux
- Signal Processing > Signal Processing Toolbox > Transforms, Correlation, and Modeling > Correlation and Convolution >
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